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PCA There is a lot of inter-correlation among the 36 original parameters. By using Principal Components Analysis, one can more efficiently represent the underlying data (in this case, silhouette faces) with fewer than 36 dimensions.

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Principal Components Analysis x-y representation

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Principal Components Analysis x-y representation

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Principal Components Analysis x-y representationPC representation

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PCA in Matlab In Matlab: e.g. X is an n by m matrix, where n = number of data points m = number of dimensions >> [pc score latent tsquare] = princomp(x); Note 1: n needs to be greater than m Note 2: it is useful to use zcore(x) instead of x

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Thank you! Slides, data, and Matlab code will be on the CSASS website: csass.ucsc.edu/short courses/index.html me with any questions or if you would like help analyzing and/or visualizing your multivariate data: